Daily House Price Indexes: Construction, Modeling, and Longer-Run Predictions

Tim Bollerslev, Andrew Patton and Wang Wenjing

No 13-29, Working Papers from Duke University, Department of Economics

Abstract: We construct daily house price indexes for ten major U.S. metropolitan areas. Our calculations are based on a comprehensive database of several million residential property transactions and a standard repeat-sales method that closely mimics the procedure used in the construction of the popular monthly Case-Shiller house price indexes. Our new daily house price indexes exhibit similar characteristics to other daily asset prices, with mild autocorrelation and strong conditional heteroskedasticity, which are well described by a relatively simple multivariate GARCH type model. The sample and model-implied correlations across house price index returns are low at the daily frequency, but rise monotonically with the return horizon, and are all commensurate with existing empirical evidence for the existing monthly and quarterly house price series. A simple model of daily house price index returns produces forecasts of monthly house price changes that are superior to various alternative forecast procedures based on lower frequency data, underscoring the informational advantages of our new more finely sampled daily price series.

Keywords: real estate; price indices; repeat sales index; high frequency data (search for similar items in EconPapers)
JEL-codes: C22 C43 R30 (search for similar items in EconPapers)
Pages: 49
Date: 2013
New Economics Papers: this item is included in nep-for and nep-ure
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Daily House Price Indices: Construction, Modeling, and Longer‐run Predictions (2016)
Working Paper: Daily House Price Indices: Construction, Modeling, and Longer-Run Predictions (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:duk:dukeec:13-29

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